In the field of drug development and discovery, there has been a long-recognized need for functional biochemical markers to validate the hypothesized phenotypic consequences of hypothesized gene or protein targets and of candidate drugs. Because of rapid technologic advances in the tools for identifying candidate targets and drugs in recent years, in particular the development of highly efficient genomic and proteomic tools for identifying potential targets of therapy (i.e., “therapeutic targets”) and highly efficient combinatorial chemistry and high-throughput screening assays for generating candidate chemical or biological therapeutic leads, the need for phenotypic screening tools to eliminate ineffective or toxic candidates has become the single greatest challenge in drug development and discovery. There are too many targets and too many lead compounds for pharmaceutical companies to pursue all of them fully. Accordingly, this overwhelming number of candidate therapeutic targets and lead compounds must be filtered, to eliminate ineffective or toxic candidates early in the drug development process. At present, however, there are no commercially available, high-throughput screening tools available for phenotypic characterization or toxicity assessment. This deficiency represents a serious gap in the pharmaceutical industry's repertoire of tools.
The capacity for vertical integration of tests used at different steps in the drug development and discovery process, ranging from pre-clinical studies in animals to human approval trials, such as FDA phase III trials, would also be extremely useful for pharmaceutical companies. Tests that could be used in the same manner and form at all levels of a drug's development process would allow comparisons across each level and internal validation of a drug's action as it advances through the development process.
Technical limitations have held back the development of high-throughput universal screening procedures for measuring biochemical phenotypes, in contrast to genotypes. The reason for this gap is that the true unit of operational function (i.e., the phenotype) in biology is not the gene or the protein in isolation, but is the dynamic flow of molecules through metabolic pathways in fully assembled systems (i.e., fluxes or kinetics of molecules). Although research techniques for measuring the concentrations of molecules (such as immunoassay, standard clinical chemistry, genomics/proteomics and other conventional high-throughput methods) are highly advanced, research techniques for measuring molecular fluxes are not as advanced. The absence of techniques for measuring molecular kinetics applies particularly for high-throughput, multiple concurrent molecular flux measurements. This deficiency represents a fundamental problem because the altered flow of molecules through complex metabolic pathways underlies essentially all diseases. (See, for example, Stephanopoulos et al.; Hellerstein, Annu Rev Nutr. 2003 cited in full, infra; Kaczer and Burns).
Conventional concentration-based assays such as clinical chemistry, immunoassays, and genomics/proteomics, are all static measurements. Molecular kinetics differs from static tests in the same way that motion pictures differ from snapshots, by including the dimension of time. A completely different set of tools is required for measuring the dynamic flow of molecules than for static measurements of molecules. Measurement techniques for molecular kinetics must involve the use of isotopic tracers, to introduce the dimension of time (see, e.g., Hellerstein and Neese, Am J Physiol 1999, cited in full, infra; Hellerstein, Annu Rev Nutr. 2003). Isotope labeling creates an asymmetry in time (the label at first is not present, then it is present) and thereby allows molecular kinetics to be determined.
Isotopic labeling techniques have typically been restricted to molecular flux rates (kinetics) of a single molecule or a single biochemical class of molecule at a time. Each labeled substrate administered is generally restricted to a single chemical class of organic molecule. By way of example, a labeled amino acid, such as 3H-leucine or 13C-lysine, can be given to label a protein or all proteins biosynthetically in the cell or organism of interest, but other classes of molecules (e.g., lipids, DNA, carbohydrates), are not usefully or reliably labeled from amino acids. Similarly, labels for measuring DNA and RNA kinetics do not allow kinetic measurements of lipids, proteins, and other classes of molecules. For this reason, previous kinetic labeling measurements have not provided information about relative molecular flux rates of multiple biological molecules of different classes, through a single protocol.
Often, it is the combinations or comparisons of different molecular flux rates that is most informative regarding biochemical consequences (phenotypes) of a drug or genetic target. (See, e.g., Hellerstein, Annu Rev Nutr. 2003; Stephanopoulos et al.). There is, however, a need to also analyze biomolecules from the same class but from different cell types or tissues because a comparison between the rate of proliferation of one type of cell versus the rate of another type of cell (e.g., tumor cells versus endothelial cells in various cancers) is frequently useful in evaluating therapeutic efficacy of a drug or disease diagnosis or prognosis. Accordingly, there exists a need to analyze and compare molecular flux rates of multiple classes of biological molecules concurrently in a simple, high-throughput manner. Furthermore, there exists a need to analyze biomolecules from the same class but existing in different cell types or tissues in a high-throughput manner.